The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for computing,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. Cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.
The main motivation is cost savings. For instance, generative AI-based search cost per query is estimated to increase by 10 times compared to traditional search methods3 – and this is just one of many generative AI applications. Hybrid AI will allow generative AI developers and providers to take advantage of the computing capabilities available in edge devices to reduce costs. A hybrid AI architecture (or running AI on a device alone) offers the additional benefits of performance, personalization, privacy, and security – at a global scale.
